4 citations · 13 across the 30 of their papers we have counts for
9 papers · 1 filter
Full-Covariance Smoothing of Bayesian Neural Networks for Online Adaptation
Oren Wright, Haoming Jing, Qiaoan Shen +3
A neural network's layers can be treated as time steps of a state-space model, turning Bayesian training into a smoothing problem: a forward pass propagates Gaussian moments throug…
Learning Fractional-Order Dynamics from a Single Trajectory
Xiaole Zhang, Ziyi Zhang, Zehao Zhao +4
Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the most recent state alone. This…
OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
Zhuoyuan Wang, Hanjiang Hu, Xiyu Deng +2
Solving diverse partial differential equations (PDEs) is fundamental in science and engineering. Large language models (LLMs) have demonstrated strong capabilities in code generati…
Kalman Bayesian Transformer
Haoming Jing, Oren Wright, José M. F. Moura +1
Sequential fine-tuning of transformers is useful when new data arrive sequentially, especially with shifting distributions. Unlike batch learning, sequential learning demands that…
Physics-Informed Deep B-Spline Networks
Zhuoyuan Wang, Raffaele Romagnoli, Saviz Mowlavi +1
Physics-informed machine learning offers a promising framework for solving complex partial differential equations (PDEs) by integrating observational data with governing physical l…
Predictive Control and Regret Analysis of Non-Stationary MDP with Look-ahead Information
Ziyi Zhang, Yorie Nakahira, Guannan Qu
Policy design in non-stationary Markov Decision Processes (MDPs) is inherently challenging due to the complexities introduced by time-varying system transition and reward, which ma…